AI Engineer Level II

Role: AI Engineer Level II
Location: Washington, DC - Onsite

As an AI Engineer (Level II), you'll design and implement enterprise-grade AI systems with a focus on Retrieval-Augmented Generation (RAG), agentic AI, and cloud-native ML pipelines. You'll work cross-functionally to operationalize secure, scalable solutions across Azure and AWS platforms, contributing to production-ready, multi-modal GenAI applications.

AI Architecture & Delivery

• Design and deploy RAG pipelines using Azure AI/Search and vector DBs (Redis, FAISS, HNSW).

• Develop conversational AI systems with prompt lifecycle management, telemetry, and guardrails.

• Integrate LLMs like Azure OpenAI, Llama, Claude, and OSS models across vision and speech domains.

Infrastructure & Orchestration

• Implement Model Context Protocol (MCP) servers with RBAC, schema versioning, validation, and audit trails.

• Deploy Azure AI Agent Service patterns: agent registry, policy enforcement, and telemetry logging.

• Use Azure Batch and AWS EMR for parallel inferencing and distributed feature processing.

Data Pipeline Engineering

• Build and manage ingestion pipelines: document normalization, metadata enrichment, PII redaction, SLA monitoring.

• Operate scalable vectorization pipelines with drift detection and quality gates.

• Use Azure Data Factory and Databricks; AWS EMR for large-scale Hadoop/Spark workloads.

Agentic AI Development

• Implement secure tool-calling and multi-agent orchestration using Semantic Kernel, AutoGen, Agent Framework, CrewAI, Agno, and LangChain.

• Apply governance, telemetry, and lifecycle management across agent runtimes with MCP controls.

Model Ops & Evaluation

• Fine-tune and evaluate OSS and proprietary models; conduct A/B tests and latency/cost analysis.

• Implement CI/CD pipelines with security scans and validation for AI/LLM workloads.

• Proficiency in CS fundamentals: algorithms, distributed systems, concurrency, networking.

• Experience with SDLC excellence: clean architecture, SOLID, testing pyramids (unit, integration, E2E).

• Secure AI app development: input validation, secret hygiene, RBAC, sandboxed functions.

• Performance engineering: latency tuning, token optimization, vector index profiling.

Azure: Azure OpenAI, AI/Search, AML, AKS, Azure Functions, Key Vault, ADF, Databricks, Azure Batch
AWS: SageMaker, Bedrock, Lambda, EMR, Comprehend, API Gateway, S3, EKS
Vector DBs: Azure AI Search, Redis, FAISS/HNSW
Frameworks: Semantic Kernel, AutoGen, Microsoft Agent Framework, CrewAI, Agno, LangChain
Inference: Docker/Ollama, vLLM, GPU provisioning, quantization (GGUF)

Education: Bachelor's in CS, Engineering, or equivalent hands-on expertise
Experience: 5+ years in software engineering; 2+ years in GenAI/LLM applications (RAG, agents, safety, eval)

• Microsoft Certified: Azure AI Fundamentals (AI-900)

• Microsoft Certified: Azure Data Fundamentals (DP-900)

• Responsible AI certifications

• AWS Machine Learning Specialty

• TensorFlow Developer

• Kubernetes CKA or CKAD

• SAFe Agile Software Engineering

Preferred:

• Azure AI Engineer Associate (AI-102)

• Azure Data Scientist Associate (DP-100)

• Azure Solutions Architect (AZ-305)

• Azure Developer Associate (AZ-204)

• GenAI architecture mastery: RAG, vector DBs, embeddings, transformer internals, multi-modal pipelines.
• Agentic systems: Azure AI Agent Service patterns, MCP servers, registry/broker/governance, secure tool-calling.
• Languages: C# and Python (production-grade), .Net, plus TypeScript for service/UI when needed.
• Azure & AWS services (see Knowledge Requirements) with hands-on implementation and operations.
• Model ops: eval suites, safety tooling, fine-tuning, guardrails, traceability.
• Business & delivery: solution architecture, stakeholder alignment, roadmap planning, measurable impact.

Desired Skills/Abilities (not required but a plus):

• LangChain, Hugging Face, MLflow; Kubernetes + GPU scheduling; vector search tuning (HNSW/IVF).
  
• Responsible AI: policy mapping, red-team playbooks, incident response for AI.
  
• Hybrid/multi-cloud deployments using Azure Arc and AWS Outposts; CI/CD for AI workloads across Azure DevOps and AWS CodePipeline.

Step into a high-impact role where AI meets cloud scalability. Apply now and bring cutting-edge solutions to life.